GAN-based Seed Generation for Efficient Fuzzing

Shyamili Toluchuri, Aishwarya Upadhyay, Smita Naval, Vijay Laxmi, Manoj Gaur

2024

Abstract

Software vulnerabilities are a substantial concern in development, with testing being crucial for identifying mistakes. Fuzzing, a prevalent technique, involves modifying a seed input to discover software bugs. Selecting the right seed is pivotal, as indicated by recent research. In our study, we extensively analyze leading gray-box fuzzing tools, applying them to identify bugs across 22 open-source applications. An innovative addition to our approach is the integration of a Deep Learning Generative Model (DCGAN). This model offers a novel method for generating seed files by learning from crash files in previous experiments. Notably, it excels in generating images across various formats, enhancing flexibility in applications with consistent input formats. The system’s primary advantages lie in its flexibility and improved fuzzing efficiency. It outperforms other applications in identifying vulnerabilities swiftly, marking a significant advancement in the current state of affairs.

Paper Citation

in Harvard Style

Toluchuri S., Upadhyay A., Naval S., Laxmi V. and Gaur M. (2024). GAN-based Seed Generation for Efficient Fuzzing. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 686-691. DOI: 10.5220/0012761900003767

in Bibtex Style

@conference{secrypt24,
author={Shyamili Toluchuri and Aishwarya Upadhyay and Smita Naval and Vijay Laxmi and Manoj Gaur},
title={GAN-based Seed Generation for Efficient Fuzzing},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={686-691},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012761900003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - GAN-based Seed Generation for Efficient Fuzzing
SN - 978-989-758-709-2
AU - Toluchuri S.
AU - Upadhyay A.
AU - Naval S.
AU - Laxmi V.
AU - Gaur M.
PY - 2024
SP - 686
EP - 691
DO - 10.5220/0012761900003767
PB - SciTePress